Acrylate Wood Densification: Effects of Vacuum Time and Nanoparticles on Chemical Retention, Penetration, and Resin Distribution
Bibliographic record
Abstract
The feasibility of preparing a surface-densified wood product by replacing the traditional time-consuming pressurization stage with only a short vacuum time was investigated. Sugar maple and red oak wood specimens were successfully impregnated with low-viscosity resins of 1,6 hexanediol dimethacrylate and trimethylolpropane trimethacrylate, with or without silicate nanoparticles, using vacuum times of 30 s to 10 min without pressurization. Chemical retention (CR) and vertical density profiles of the treated wood specimens were measured. The CRs obtained with the short vacuum impregnation process, even with a vacuum of 30 s or 60 s, proved comparable to those achieved by the traditional process of 30-min vacuum plus 30-min pressure. A 52-63 wt% CR was found for maple impregnated with neat resin, while the formulations containing nanoparticles achieved 44-55 wt% as the vacuum time was increased 30 s to 10 min. Oak yielded lower CR values. The vertical density profiles indicated better treatability for maple than oak. Examination of the resin and resin/nanoparticle penetration into the wood by scanning electron microscopy revealed successful wood impregnation with both nanoparticles and resin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".